Artificial intelligence in nanotechnology for treatment of diseases.

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Application of artificial intelligence in chronic myeloid leukemia (CML) disease prediction and management: a scoping review.

BACKGROUND: Navigating the complexity of chronic myeloid leukemia (CML) diagnosis and management poses significant challenges, including the need for accurate prediction of disease progression and response to treatment. Artificial intelligence (AI) presents a transformative approach that enables the development of sophisticated predictive models and personalized treatment strategies that enhance early detection and improve therapeutic interventions for better patient outcomes. METHODS: An extensive search was conducted to retrieve relevant articles from PubMed, Scopus, and Web of Science databases up to April 24, 2023. Data were collected using a standardized extraction form, and the results are presented in tables and graphs, showing frequencies and percentages. The authors adhered to the PRISMA-ScR checklist to ensure transparent reporting of the study. RESULTS: Of the 176 articles initially identified, 12 were selected for our study after removing duplicates and applying the inclusion and exclusion criteria. AI's primary applications of AI in managing CML included tumor diagnosis/classification (n = 9, 75%), prediction/prognosis (n = 2, 17%), and treatment (n = 1, 8%). For tumor diagnosis, AI is categorized into blood smear image-based (n = 5), clinical parameter-based (n = 2), and gene profiling-based (n = 2) approaches. The most commonly employed AI models include Support Vector Machine (SVM) (n = 5), eXtreme Gradient Boosting (XGBoost) (n = 4), and various neural network methods, such as Artificial Neural Network (ANN) (n = 3). Furthermore, Hybrid Convolutional Neural Network with Interactive Autodidactic School (HCNN-IAS) achieved 100% accuracy and sensitivity in organizing leukemia data types, whereas MayGAN attained 99.8% accuracy and high performance in diagnosing CML from blood smear images. CONCLUSIONS: AI offers groundbreaking insights and tools for enhancing prediction, prognosis, and personalized treatment in chronic myeloid leukemia. Integrated AI systems empower healthcare practitioners with advanced analytics, optimizing patient care and improving clinical outcomes in CML management.

The efficacy of virtual reality for upper limb rehabilitation in stroke patients: a systematic review and meta-analysis.

BACKGROUND: Stroke frequently gives rise to incapacitating motor impairments in the upper limb. Virtual reality (VR) rehabilitation has exhibited potential for augmenting upper extremity recovery; nonetheless, the optimal techniques for such interventions remain a topic of uncertainty. The present systematic review and meta-analysis were undertaken to comprehensively compare VR-based rehabilitation with conventional occupational therapy across a spectrum of immersion levels and outcome domains. METHODS: A systematic search was conducted in PubMed, IEEE, Scopus, Web of Science, and PsycNET databases to identify randomized controlled trials about upper limb rehabilitation in stroke patients utilizing VR interventions. The search encompassed studies published in the English language up to March 2023. The identified studies were stratified into different categories based on the degree of immersion employed: non-immersive, semi-immersive, and fully-immersive settings. Subsequent meta-analyses were executed to assess the impact of VR interventions on various outcome measures. RESULTS: Of the 11,834 studies screened, 55 studies with 2142 patients met the predefined inclusion criteria. VR conferred benefits over conventional therapy for upper limb motor function, functional independence, Quality of life, Spasticity, and dexterity. Fully immersive VR showed the greatest gains in gross motor function, while non-immersive approaches enhanced fine dexterity. Interventions exceeding six weeks elicited superior results, and initiating VR within six months post-stroke optimized outcomes. CONCLUSIONS: This systematic review and meta-analysis demonstrates that adjunctive VR-based rehabilitation enhances upper limb motor recovery across multiple functional domains compared to conventional occupational therapy alone after stroke. Optimal paradigms likely integrate VR's immersive capacity with conventional techniques. TRIAL REGISTRATION: This systematic review and meta-analysis retrospectively registered in the OSF registry under the identifier [ https://doi.org/10.17605/OSF.IO/YK2RJ ].

Waiting time for Mohs Micrographic Surgery and the associated increase in lesion size of basal cell carcinoma.

BACKGROUND: The first-line treatment for basal cell carcinoma (BCC) involved either surgical excision (SE) or Mohs micrographic surgery (MMS). The current waiting time for MMS and whether this is associated with worsening of outcome is unknown. We aim to look at the waiting time for MMS upon review at the Outpatient Specialist Dermatology Clinic and the increase in lesion size during this time period. METHODS: A retrospective analysis of all patients who underwent MMS for biopsy-proven BCC over a duration of 5 years was performed. Variables analysed included patient age, sex, location, histologic subtype, lesion size at initial presentation, lesion size during MMS, defect size following MMS, waiting time for MMS, and projected defect size if lesions were treated with SE at earlier time point. RESULTS: A total of 233 patients were included in the final analysis. The mean wait time between referral to MMS was 215.8±125.7 days. The mean maximum diameter of lesions was 9.34±5.23 mm at referral for MMS and 11.91±6.60 mm at presentation for MMS, hence an increase of 3.01±3.03 mm (p=0.001, paired t-test), which equates to 1.41±0.42 times increase. The mean maximum diameter of the actual defect size post-MMS was 18.03±9.45mm, while the projected diameter of defect post SE was 15.29±5.71 mm, hence, smaller by 3.27±5.51 mm (p=0.001, paired t-test) compared with actual defect size post-MMS. CONCLUSION: This study highlights that the lesion grows significantly during the long wait for MMS. Hence, an increase in capacity for MMS is essential to reduce the waiting time for surgery which could lead to a defect which is easier to reconstruct with better outcome for patients.